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Depth separation beyond radial functions
v1v2v3v4 (latest)

Depth separation beyond radial functions

Journal of machine learning research (JMLR), 2021
2 February 2021
Luca Venturi
Samy Jelassi
Tristan Ozuch
Joan Bruna
ArXiv (abs)PDFHTML

Papers citing "Depth separation beyond radial functions"

13 / 13 papers shown
Spectral complexity of deep neural networks
Spectral complexity of deep neural networksSIAM Journal on Mathematics of Data Science (SIMODS), 2024
Simmaco Di Lillo
Domenico Marinucci
Michele Salvi
Stefano Vigogna
BDL
526
3
0
15 May 2024
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
Suzanna Parkinson
Greg Ongie
Rebecca Willett
Ohad Shamir
Nathan Srebro
MDE
268
2
0
13 Feb 2024
Depth Separations in Neural Networks: Separating the Dimension from the
  Accuracy
Depth Separations in Neural Networks: Separating the Dimension from the AccuracyAnnual Conference Computational Learning Theory (COLT), 2024
Itay Safran
Daniel Reichman
Paul Valiant
317
2
0
11 Feb 2024
How Many Neurons Does it Take to Approximate the Maximum?
How Many Neurons Does it Take to Approximate the Maximum?ACM-SIAM Symposium on Discrete Algorithms (SODA), 2023
Itay Safran
Daniel Reichman
Paul Valiant
322
11
0
18 Jul 2023
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function SpaceJournal of machine learning research (JMLR), 2023
Zhengdao Chen
502
4
0
03 Jul 2023
Depth Separation with Multilayer Mean-Field Networks
Depth Separation with Multilayer Mean-Field NetworksInternational Conference on Learning Representations (ICLR), 2023
Y. Ren
Mo Zhou
Rong Ge
OOD
315
5
0
03 Apr 2023
Expressivity of Shallow and Deep Neural Networks for Polynomial
  Approximation
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation
Itai Shapira
202
0
0
06 Mar 2023
Exponential Separations in Symmetric Neural Networks
Exponential Separations in Symmetric Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
Aaron Zweig
Joan Bruna
359
10
0
02 Jun 2022
Width is Less Important than Depth in ReLU Neural Networks
Width is Less Important than Depth in ReLU Neural NetworksAnnual Conference Computational Learning Theory (COLT), 2022
Gal Vardi
Gilad Yehudai
Ohad Shamir
3DV
192
13
0
08 Feb 2022
Depth and Feature Learning are Provably Beneficial for Neural Network
  Discriminators
Depth and Feature Learning are Provably Beneficial for Neural Network DiscriminatorsAnnual Conference Computational Learning Theory (COLT), 2021
Carles Domingo-Enrich
MLTMDE
238
0
0
27 Dec 2021
Optimization-Based Separations for Neural Networks
Optimization-Based Separations for Neural NetworksAnnual Conference Computational Learning Theory (COLT), 2021
Itay Safran
Jason D. Lee
772
19
0
04 Dec 2021
On the Optimal Memorization Power of ReLU Neural Networks
On the Optimal Memorization Power of ReLU Neural Networks
Gal Vardi
Gilad Yehudai
Ohad Shamir
209
42
0
07 Oct 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability
  Metrics
Separation Results between Fixed-Kernel and Feature-Learning Probability MetricsNeural Information Processing Systems (NeurIPS), 2021
Carles Domingo-Enrich
Youssef Mroueh
283
1
0
10 Jun 2021
1
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